Author

Markus Götz

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#artificial intelligence Preprint Sep 2026

4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computational bottlenecks, we devise an orthogonal 4D-parallelization scheme that introduces a unique...

Deifilia Kieckhefen, J. P. G. H. Muriedas, L. Heyen et al. · 0 citations

Open access Sep 2026

NucleicBERT interprets RNA sequence space through self-supervised language modelling

NucleicBERT is developed, a self-supervised masked-language model that learns contextual representations from single sequences without evolutionary information that advances RNA structure prediction and informs how large language models encode biological information.

Utkarsh Upadhyay, Julian Herold, Markus Götz et al. · Nature Machine Intelligence · 1 citation